1. Field of the Invention
The present invention relates generally to demand forecasting systems.
2. Discussion of the Related Art
Most businesses use forecasting techniques to predict the future demand for their products or services. Anticipating these sales helps such businesses optimize inventory levels, adjust staffing requirements, or even set production schedules. A convenience store expecting heavy traffic for a summer holiday weekend, for example, might hire an extra employee and stock more cold beverages in anticipation of the higher level of sales. Automobile manufacturers might add a second shift to an assembly line if the demand for cars is expected to increase. Accurate forecasts allow companies to more appropriately allocate resources.
One way that sellers have attempted to predict demand is by projecting past sales into the future. For example, the airline industry uses past load factor data in order to help set current and future ticket prices. However, such projections of past data are speculative at best in that market conditions can change dramatically, rendering the historic data useless. There is simply no guarantee that the future is going to be similar to the past. Accordingly, projecting future sales based on past results is simply not a reliable forecasting tool for many businesses. Furthermore, such demand forecasting systems predict aggregate demand, rather than demand of individual customers.
Another method for attempting to predict demand is to try to anticipate new trends in the market. For example, a car manufacturer might predict that luxury vehicles are going to be more popular if the stock market experiences a substantial increase in value. Such projections are tenuous, however, since they ultimately rely on a past trend (luxury sales correlated with higher stock prices) continuing into the future.
Some businesses have limited access to demand data in the form of customer input. Many electronic commerce sites, for example, allow customers to place goods into “virtual shopping carts.” Such shopping carts allow customers to store indications of products that they intend to purchase. Some demand information can be derived from which items are stored in these shopping carts, but e-commerce merchants do not know if the customers are actually going to purchase the products. There is no way for the business to know which items will actually be purchased.
Traditionally, prospective buyers have had little or no reason or method of informing sellers of their desire to purchase products before they actually purchase products, thus depriving sellers of useful consumer demand information. Rather, the only way that sellers have traditionally been able to collect such demand data is after customers have purchased products.